Disease Outbreak /Epidemic in Public Health Sector

Theodor Bilharz Research Institute

Bibliographic Information

Authors: Abdallah R.; AbdelGaber S.A.; Ali Sayed H.

Journal: 6th International Conference on Computing and Informatics, ICCI 2024

Publisher: Institute of Electrical and Electronics Engineers Inc.

Publication Date: 6 March 2024

Pages: 203–216

DOI: 10.1109/ICCI61671.2024.10485007

Scopus: View on Scopus

Document Type: Conference paper

Authors and Affiliations

Abdallah R., Faculty of Business, Economics and Business Technology, Egyptian Russian University, Cairo, Egypt; AbdelGaber S.A., Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt; Ali Sayed H., Public Health deparment, Theodor Bilharze Research Institute, Faculty of Medicine, Helwan University, Cairo, Egypt

Abstract

In recent eras, the COVID-19 pandemic has become a global phenomenon, significantly impacting efforts to anticipate the disease outbreak in its early stages. Hence, timely detection and analysis within the public health sector can lead to early control of outbreaks/epidemics. Due to a high level of uncertainty and lack of outbreak essential data, standard models have shown low accuracy for long-term prediction. Although the literature review includes several attempts to address this issue, the essential generalization and robustness of the abilities of existing models need to be improved. After investigating the most recent studies, health-related data can be analyzed and interpreted using Machine Learning (ML) techniques to identify potential disease outbreaks/epidemics, facilitate prompt treatment, and ultimately result in cost savings for medical care. This research aims to assess the performance and predictive capabilities of different machine learning algorithms to identify the most accurate and reliable models for disease prediction. The results of this research are to propose a novel framework for early outbreak/epidemic detection using ML techniques. Additionally, conduct a comparative analysis of studies that have utilized ML techniques to detect disease outbreaks. © 2024 IEEE.

Keywords

Epidemic; Infectious Diseases; Machine Learning; Outbreak; Public Health; Risk Factors; Chemical detection; Disease control; Diseases; Health risks; Learning algorithms; Data standards; Disease outbreaks; Infectious disease; Machine learning techniques; Machine-learning; Standard model; Uncertainty

Citation Information

Scopus Citations: 4

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